In three new publications, we explore critical issues in AI safety and bias. In the first paper, we audit popular Stable Diffusion models and reveal stark safety failures, including racial bias and violent content generation, underscoring the need for stronger safeguards in these models. In the second paper, I argue that alignment objectives like helpfulness and harm avoidance inherently embed progressive moral values, making true political neutrality both impractical and potentially detrimental to AI alignment. Finally, the third paper introduces a lightweight, LoRA-based method for mitigating identity-related biases in LLMs, achieving substantial fairness improvements without full-model retraining. Together, I hope that these works push forward the conversation on building AI systems that are not only powerful but also responsible, inclusive, and aligned with the right values.